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Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment.

Dutta R, Dutta R - Biomed Eng Online (2006)

Bottom Line: In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA).IBC outperformed MLP, PNN and RBFN.We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Reading, Reading RG6 6AY, UK. r.dutta@reading.ac.uk

ABSTRACT
Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO2) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment.

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Related in: MedlinePlus

PCA component plot of the data using "area under the curve" as feature.
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Figure 3: PCA component plot of the data using "area under the curve" as feature.

Mentions: We have done PC analysis using "area beneath the curve", "kurtosis" and "skewness" as feature and plotted first three significant PCA components to estimate characteristics of the data. Feature "area beneath the curve" based PCA results indicated that the data from different classes are closely overlapped in some regions and they do have a reasonable amount of outliers to think about them. See figure 3 where specifically S. aureus PCA scores are overlapped heavily with E. coli PCA scores. Microbiologically it's true that E. coli and S. aureus strains should have similar characteristics (they can live together) and that feature is also reflected in our initial PCA results. The overlapping clusters have significant effect on over all classification and it also increases the rate of misclassifications.


Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment.

Dutta R, Dutta R - Biomed Eng Online (2006)

PCA component plot of the data using "area under the curve" as feature.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1764885&req=5

Figure 3: PCA component plot of the data using "area under the curve" as feature.
Mentions: We have done PC analysis using "area beneath the curve", "kurtosis" and "skewness" as feature and plotted first three significant PCA components to estimate characteristics of the data. Feature "area beneath the curve" based PCA results indicated that the data from different classes are closely overlapped in some regions and they do have a reasonable amount of outliers to think about them. See figure 3 where specifically S. aureus PCA scores are overlapped heavily with E. coli PCA scores. Microbiologically it's true that E. coli and S. aureus strains should have similar characteristics (they can live together) and that feature is also reflected in our initial PCA results. The overlapping clusters have significant effect on over all classification and it also increases the rate of misclassifications.

Bottom Line: In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA).IBC outperformed MLP, PNN and RBFN.We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Reading, Reading RG6 6AY, UK. r.dutta@reading.ac.uk

ABSTRACT
Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO2) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment.

Show MeSH
Related in: MedlinePlus